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MACHINE LEARNINGDescribe the evaluation metrics used in machine learning, such as accuracy, precision, recall, and F1-score, with a numerical example. Explain the importance of cross-validation in estimating a model's generalization performance.20257mData MiningThe classification and prediction methods are affected by (i) accuracy (ii) speed (iii) robustness (iv) All of the above20222mData MiningDefine Classification and Prediction. List the major issues in Classification and Prediction.20257mMACHINE LEARNINGFollowing is a data set that contains two attributes, X and Y, and two class labels, ‘+’ and ‘–’. Each attribute can take three different values: 0, 1, or 2. The concept for the ‘+’ class is Y = 1 and the concept for the ‘–’ class is X = 0 ∨ X = 2.  (a) Build a decision tree on the data set. Does the tree capture the ‘+’ and ‘–’ concepts? (b) What are the accuracy, precision, recall and F1-measure of the decision tree? (Note that precision, recall, and F1-measure are defined with respect to the ‘+’ class.)202214m